Francesco Gentile

The paper uses machine learning-augmented virtual screening to discover a novel series of Mac1 inhibitors for SARS-CoV-2 NSP3, validated by experimental assays and crystallography.

The paper develops a multimodal machine learning framework to predict ADC cytotoxicity by integrating structural parameters with tumor cell biology data.

The paper evaluates computational hit-finding methods for targeting the WDR domain of LRRK2, a Parkinson's disease protein, finding that diverse strategies yielded weakly potent compounds.

This paper uses AI to develop risk scores for predicting MASLD progression by integrating genetic, anthropometric, and mitochondrial biomarkers.

The paper develops 4f-SAMMY-seq to map euchromatin and heterochromatin based on accessibility and solubility, and reconstructs their 3D compartmentalization in one experiment.

PROTACable automates PROTAC design using 3-D modeling and deep learning to predict activity of newly designed PROTACs.

The paper introduces Deep Docking (D2), a deep learning method for virtual screening of large chemical datasets, achieving significant data reduction and enrichment of top hits.